Compressed Sensing（ CS） in sparse microwave imaging has been extensively investigated. Inverse Synthetic Aperture Radar（ ISAR） imaging problem can be transformed into a sparse signal representation problem. However

the performance of current CS-based ISAR imaging algorithms is usually limited in terms of reconstruction speed and accuracy; as such

existing algorithms are inconvenient for practical applications. Accelerated Iterative Hard Thresholding（ AIHT） can be used to establish a trade-off between low computational complexity and strong performance guarantees in CS. Considering this condition

we determined the mechanism by which AIHT algorithm is applied to ISAR sparse imaging.In ISAR imaging

cross-range resolution is dependent on the total rotation angle of a target relative to the radar line of sight during observation. In a short observation time

high cross-range resolution is difficult to obtain using a conventional ISAR imaging algorithm because target-radar orientation variation is usually small. The CS method can be applied in ISAR imaging to obtain highresolution images with limited measurements because space distribution of point scatterers is sparse. After an azimuth Fast Fourier Transform（ FFT） is obtained

an echo signal can be expressed with sparse discrete points in a Doppler frequency domain. Based on CS theory

this study establishes an ISAR sparse imaging model

which contains an undersampling echo data in azimuth. AIHT is robust to noise to some extent; thus

this algorithm can obtain enhanced reconstruction results in a few number of observations.The AIHT method is also simple to implement and does not require computation

storage

and repeated use of matrix inverses.Therefore

this method provides an advantage in numerous CS applications characterized by measurement matrix that is often based on fast transforms

such as wavelet and Fourier transform. This study also compares various CS algorithms and rationally analyzes the specific features and adaptive mechanism of the AIHT algorithm applied to ISAR sparse imaging problem.Experimental results based on simulated and measured data show that the proposed algorithm maintains a more efficient balance between computation load and reconstruction signal sparsity than existing algorithms. The echo data in simulation experiments are added to Gauss white noise with 4 d B SNR; thus

half of the azimuth data are randomly selected and missing data are zero padded. A one-dimensional range profile is obtained through range alignment and phase correction; a two-dimensional ISAR image is then obtained through the azimuth processing. Results suggest that this method can effectively achieve imaging with few measurements of a complex baseband echo signal. This method is also focused on a large variation range of pulse number and signal-to-noise ratio. The advantage of reconstruction speed is apparent compared with other CS algorithms.This study shows that the ISAR sparse imaging method based on AIHT not only significantly reduces the time of imaging reconstruction but also improves the precision of imaging reconstruction. AIHT also exhibits a great advantage of processing real-time imaging or large-scale and high-dimension problems. Step selection and setting threshold strategy should be improved in further studies to maintain a more robust algorithm and avoid local minimum.